kmeans-node

A javascript implementation of k-means clustering algorithm that includes one/two dimentional arrays, and supports objects with coordinates.

Install

You can install kmeans-node using NPM:

npm install kmeans-node

Usage

In order to use kmeans-node, you must first import it:

var kmeans = require('kmeans-node');

Once kmeans has been imported, you can call kmeans on several types of data:

• one-dimensional arrays
• two-dimensional arrays
• arrays of objects

One-dimensional array

A one-dimensional array can be clustered by calling kmeans.array(array, numberOfMeans)

var data = [1,2,4,8,10,14,18,20];
var kMeansObject = kmeans.array(data, 4);

Two-dimensional array

A two-dimensional array can be clustered by calling kmeans.array2d(arrayOfArrays, numberOfMeans)

var data = [[1,2],[3,4],[7,8],[9,10],[13,14],[15,16],[22,23],[24,25]];
var kMeansObject = kmeans.array2d(array,4);

Array of objects

It is possible to use this method, if points are releated with data. E.g. when grouping coordinates with other properties such as shopping malls, cafes, etc .. The method can be called by kmeans.object(arrayOfObjects, numberOfMeans)

var data = [
  {x:1,y:2,data:"some data"},
  {x:3,y:4,data:"some data"},
  {x:7,y:8,data:"some data"},
  {x:9,y:10,data:"some data"},
  {x:13,y:14,data:"some data"},
  {x:15,y:16,data:"some data"},
  {x:22,y:23,data:"some data"},
  {x:24,y:25,data:"some data"}
];
var kMeansObject = kmeans.object(array, 4);

Output

The result output of clustering is an array of objects. The output structure varies depending on the input data structure.

One-dimensional array

{ 
  value: "median", 
  sum: "sum of distance", 
  pre: "previous distance", 
  points: "array of mean's points" 
}

Two-dimensional array

{
  values: "array of length 2 that contains medians",
  sum: "sum of distance",
  pre: "previous distance",
  sum0: "distance of array[0]",
  sum1: "distance of array[1]",
  points: "array of mean's points"
}

Array of objects

{   
    x: "median of X",
    y: "median of Y",
    sum: "sum of distance",
    pre: "previous distance",
    sumX: "distance of X",
    sumY: "distance of Y",
    points: "array of mean's points" 
}

MIT License

The MIT License (MIT)

Copyright (c) 2015 Abdullah Shahin

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.